前一段时间写了一些程序，自己搭建卷积神经网络的时候需要用到不少tensorflow的自带低级封装还有一些常用的读取操作等，虽然已经有了keras等高级封装可以直接调用，但还是想自己写一写。

tensorflow版本：1.14

1. 卷积函数

def weight_variable(shape):  
    initial = tf.truncated_normal(shape, mean=0, stddev=0.01)#正态初始化  
    return tf.Variable(initial)

def bias_variable(shape):  
    initial = tf.constant(0.1, shape=shape)  
    return tf.Variable(initial)

def conv2d(x, W):#步长为1   
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def conv(x,next_depth):   
    depth = x.get_shape()[-1].value 
    w = weight_variable([3, 3, depth, next_depth])   
    b = bias_variable([next_depth])   
    r = tf.nn.relu(tf.nn.bias_add(conv2d(x, w), b))  
    return r

2. 池化函数

def max_pool(x):   
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

3. 读取图片顺便加标签

• 文件夹结构（假设目录下面有17个文件夹，每个文件夹里面有若干对应种类的图片）：

  ./
  
  –./dogs
  
  –./cats
  
  –./tigers
  
  …

• 读取的图片压缩为32323的尺寸，便于神经网络的快速迭代

# 调用skimage的函数
from skimage.io import imread
from skimage.transform import resize

def load_images(path):   
    contents = os.listdir(path)   
    classes = [each for each in contents if os.path.isdir(os.path.join(path,each))]   
    print('目录下有%s' % classes)  
    
    # 用labels来存储图片的类别    
    labels = []  
    # images数组用来存储图片数据   
    images = []   
    
    # 对每个不同种类读取图片到list并且+标签   
    for each in classes:   
        class_path = os.path.join(path,each)  
        files = os.listdir(class_path)    
        print("Starting {} images".format(each),'数量为',len(files))     
        for ii, file in enumerate(files, 1):     
            # 载入图片并放入batch数组中        
            img = imread(os.path.join(class_path, file))      
            img = img / 255.0         
            # print(img.shape)      
            img = resize(img, (32, 32))     
            # print(img.shape)      
            images.append(img.reshape((32,32,3)))      
            labels.append(each)                      
	
    images = np.array(images)  
    #将labels的list形式转换为one_hot_label   
    from sklearn.preprocessing import LabelBinarizer 
    lb = LabelBinarizer()   
    lb.fit(labels)  
    labels_vecs = lb.transform(labels)   
    print('总共读取了%d张图片'%images.shape[0])  
    # print(labels,labels_vecs)  
    return images,labels_vecs # 返回图片以及对应的标签

4. 神经网络模型的建立

下面的示例结构为：3层卷积+2层全连接

图片的输入x_images的shape是32323

def create(x_images,keep_prob):#x_images的shape是32,32,3 
    h = 32  
    w = 32  
    d = 3   
    #first layer   
    conv1 = conv(x_images,16) 
    pool1 = max_pool(conv1)
    h = int(np.ceil(h/2)) 
    w = int(np.ceil(w/2))  
    d = 16    
    
    #second layer   
    conv2 = conv(pool1,32)   
    pool2 = max_pool(conv2) 
    h = int(np.ceil(h/2))  
    w = int(np.ceil(w/2)) 
    d = d*2       
    
    #third layer  
    conv3 = conv(pool2,64) 
    pool3 = max_pool(conv3)  
    h = int(np.ceil(h/2))  
    w = int(np.ceil(w/2))   
    d = d*2   
    
    #first fully layer 
    w_fc1 = weight_variable([h*w*d,1024])
    b_fc1 = bias_variable([1024]) 
    h_conv_flat = tf.reshape(pool3,[-1,h*w*d])  
    h_fc1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_conv_flat, w_fc1), b_fc1))   
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    #second fully layer   
    w_fc2 = weight_variable([1024,1024])   
    b_fc2 = bias_variable([1024])  
    h_fc2 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_fc1_drop, w_fc2), b_fc2)) 
    h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)      
    
    #third full layer  
    w_fc3 = weight_variable([1024,17])  
    b_fc3 = bias_variable([17])   
    h_fc3 = tf.add(tf.matmul(h_fc2_drop, w_fc3), b_fc3,name='h_fc3')   
    out = tf.nn.softmax(h_fc3,name='out')     
    print('模型建立好了！')  
    return out